Machine training for native language and fluency identification

ABSTRACT

Training a machine by a machine learning technique for recognizing speech utterance to determine language fluency level of a user. Native speaker recorded data and language specific dictionary of heteronyms may be retrieved. The native speaker recorded data may be parsed and the heteronyms from the native speaker recorded data may be isolated. Linguistic features from the native speaker recorded data including at least linguistic features associated with the heteronyms may be extracted, and a language dependent machine learning model is generated based on the linguistic features.

FIELD

The present application relates generally to computers and computerapplications, and more particularly to speech recognition, automaticlanguage fluency recognition and machine learning.

BACKGROUND

Systems exist that perform automatic language or speech recognition, forexample, using classification technique and/or machine learningtechnique such as neural networks. Language spoken is one dimension thatmay be used to test fluency in that language. Learning how nativespeakers speak their native language, for example, may aid in enhancingthe fluency of spoken language for users trying to learn that language.

BRIEF SUMMARY

A method and system for machine training for native language and fluencyidentification may be provided. The method may be executed by one ormore hardware processors. The method, in one aspect, may includetraining a machine by a machine learning technique for recognizingspeech utterance to determine language fluency level of a user. Thetraining, in one aspect, may include retrieving native speaker recordeddata from a database. The training may also include retrieving languagespecific dictionary of heteronyms. The training may also include parsingthe native speaker recorded data and isolating the heteronyms from thenative speaker recorded data. The training may also include extractinglinguistic features from the native speaker recorded data including atleast linguistic features associated with the heteronyms. The trainingmay also include generating a language dependent machine learning modelbased on the linguistic features. The method may also include generatinga test corpus of words comprising at least the heteronyms. The methodmay further include receiving a test speech utterance of a user utteringthe test corpus of words. The method may also include inputting the testspeech utterance to the language dependent machine learning model andexecuting the language dependent machine learning model. The languagedependent machine learning model may output a score representing thelanguage fluency level of the user.

A system of training a machine that recognizes native speech utterance,in one aspect, may include a hardware processor and a storage devicecommunicatively coupled to the hardware processor and storing nativespeaker recorded data. The hardware processor may execute a machinelearning technique to train the hardware processor to recognize speechutterance to determine language fluency level of a user. The trainingmay include the hardware processor retrieving native speaker recordeddata from the storage device, retrieving language specific dictionary ofheteronyms, parsing the native speaker recorded data and isolating theheteronyms from the native speaker recorded data, extracting linguisticfeatures from the native speaker recorded data including at leastlinguistic features associated with the heteronyms, and generating alanguage dependent machine learning model based on the linguisticfeatures. The hardware processor may further generate a test corpus ofwords comprising at least the heteronyms. The hardware processor mayfurther receive a test speech utterance of a user uttering the testcorpus of words. The hardware processor may also input the test speechutterance to the language dependent machine learning model and executethe language dependent machine learning model. The language dependentmachine learning model may output a score representing the languagefluency level of the user.

A computer readable storage medium storing a program of instructionsexecutable by a machine to perform one or more methods described hereinalso may be provided.

Further features as well as the structure and operation of variousembodiments are described in detail below with reference to theaccompanying drawings. In the drawings, like reference numbers indicateidentical or functionally similar elements.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example of use case scenario for presenting testmaterial, for example, generated according to a system and method of thepresent disclosure in one embodiment.

FIG. 2 is a flow diagram illustrating a method of training a machine tolearn to generate a test corpus in one embodiment of the presentdisclosure.

FIG. 3 is a diagram illustrating components of a system that may train amachine to learn to generate a test corpus and presenting the testcorpus to a user for evaluation.

FIG. 4 illustrates a schematic of an example computer or processingsystem that may implement a training system in one embodiment of thepresent disclosure.

DETAILED DESCRIPTION

There are many benefits in human-computer interaction that come fromknowing the native language of the end-user. For example, speechinterfaces can be tuned to native or colloquial vocabularies andutterances, and multi-user interfaces can be personalized for differentlanguage capabilities of the users. Examples include smart-phone apps orapplications that use speech input to query information from mappingapplications, real-time translation applications, and spoken dialogueagent interfaces that support personal assistant capabilities (e.g.,calendar, scheduling, information look-up). In this disclosure, asystem, method and technique may be presented that embodies variouslanguage processes that can be analyzed linguistically to determinewhether a user is a native (or fluent) speaker of a specific language(e.g., Portuguese).

A person's native language (also referred to as a first language ormother tongue) is one that a person has learned and spoken from birth.The speaker usually has a deeply nuanced understanding of the nativelanguage and is often considered an authority on many aspects oflanguage use. Having quick and accurate methods allow identifying of thenative language of a user in a human-machine system. First, it allowsfor the customization of the user interface to match the most natural(native) language of the user. This allows for a more natural interface.Second, for kinds of data acquisition tasks, the quality of the inputmay depend on different fluency level of a speaker. An example of such adata acquisition task may include crowd-sourced language transcriptionor translation. Another example may include crowd-sourced labeling(text) of images, which often provides an important measure of groundtruth for machine-learning classifiers, which benefits from users whohave a desired level of fluency in a language.

The benefits provided by the system, method and technique of the presentdisclosure may include, but are not limited to: Assessment protocols ortechniques to determine the language fluency of a speaker, using teststhat focus on different linguistic skills. These linguistic skillsinclude, for example, correct pronunciation of heteronyms presented incontext, or identification of same and/or different meaning forambiguous utterances; Application of certified and/or qualified highlyfluent speakers to crowd-sourcing tasks (e.g., transcription,translation or content labeling); Certification of language fluency forvarious job-related purposes.

A system and a method in the present disclosure in one embodimentdetermine the language fluency of an individual based on fluency testsets that differentiate native from non-native speakers. These test setsmay be developed by highly trained language experts or developed usingmachine learning-based classifiers.

The system, for example, provides a human-machine interaction thatcollects data from the individual about detailed and nuanced languageuse. The test material may be constructed using particular languageconstructs (e.g., heteronyms) that are difficult for non-native ornon-fluent speakers of a language, but are relatively easy for a nativeof highly fluent speaker. The test material is highly based onlinguistic knowledge, that is, different and specific linguisticknowledge may be used (individually or combined) in order to provide ascore from different linguistic skills: phonetics (pronunciation andaccent), spelling, vocabulary complexity, tone, morphology, syntax,semantics, pragmatics and cultural use of language.

In more complex tests of language fluency, rule based and corpora basedapproaches may be also used. One or more machine learning and statisticstechniques create the test corpus from general utterance examples, andalso score the performance of the user on the complex tests.

Examples of machine learning and statistics techniques may include:Supervised learning such as decision trees, support vector machines,neural networks, convolution neural networks, case based reasoning,k-nearest neighbor; Unsupervised learning such as deep learning,self-organizing maps, k-means algorithm, expectation maximization;Statistic based learning such as logistic regression, Naive Bayes,discriminant analysis, isotonic separation; and/or other techniques suchas genetic algorithms, group method, fuzzy sets, and rules-based.

Each performance result may have an associated weight that is adjustedand improved over time calibrating the mode with the arrival of newinformation.

FIG. 1 illustrates an example of use case scenarios for presenting testmaterial, for example, generated according to a system and method of thepresent disclosure in one embodiment. The method may performed by atleast one hardware processor. Referring to FIG. 1, at 102, aninstruction may be presented to a user via a user interface. Theinstruction tells the user what action the user is to perform on theuser interface. For example, an instruction may be to read the contentdisplayed on the user interface aloud. At 104, the user interfacepresents a display of content with an embedded heteronym. For example:“I close the door close to me.” Briefly, a heteronym is a word that iswritten identically but has a different pronunciation and meaning. At106, the user interface prompts the user to read the displayed content,for example, a sentence out load. At 108, responsive to the user readingthe content aloud or vocalizing the content, the user interface capturesthe user's speech utterance.

At 110, the user's pronunciation of the embedded heteronyms is isolatedfrom the captured user's speech utterance. At 112, linguistic featuresare extracted from the target speech and scored using the heteronymlanguage model. A fluency score is generated and the score associatedwith the user is incremented. For instance, initially the fluency scoremay be set at zero or another initial value. As the user pronounces theheteronyms correctly (i.e., the extracted features are scored above athreshold in the heteronym language model, the score may be updated. At114, next utterance is presented and the processing at 106, 108, 110 and112 may be repeated. The processing may iterate for differentutterances. At 116, total score is determined for the user, for example,as the aggregated score from performing each of the utterances. At 118,if the user's score exceeds or meets a threshold, the user is determinedto be highly fluent, and may be certified.

Examples of heteronyms, for example, in English language, may include(but are not limited to): My house and his apartment are enough to houseeverybody this weekend; I close the door close to me; After preparingthe extract, you should extract the lighter substances; That is anelaborate project. Could you, please, elaborate on the goals; The globalcrisis and the decrease of investments will decrease the jobsopportunities as well.

FIG. 2 is a flow diagram illustrating a method of training a machine tolearn to generate a test corpus in one embodiment of the presentdisclosure. The method may be performed by one or more hardwareprocessors. At 202, a machine is trained by a machine learning techniqueto create a language model that can be used to evaluate the languagefluency level of a user. For example, a machine learning model isgenerated based on selected features extracted from the speechutterance. These features may include phoneme duration, phoneme chains,intonation, timing, and loudness.

At 204, a test corpus of words may be created, for example, includingwords used in training the machine learning model. This corpus in oneembodiment includes sentences with embedded heteronym pairs, and theassociated feature parameters for the different pronunciation of thewords.

At 206, the test corpus is presented via a user interface display. Forexample, test content described with reference to FIG. 1 may bepresented.

At 208, user input is received via the user interface display. Forexample, user speech spoken through a microphone may be captured. User'sanswer entered on the user interface may be read and captured.

At 210, the machine learning model is executed and the user input isscored to determine the language fluency level of the user. For example,the machine learning model may output a score that indicates thelanguage fluency level of the user.

At 212, the machine is retrained. For example, the machine learningmodel may be readjusted based on the user's input. In one embodiment,retraining may be done by recreating the heteronym language model usingadditional speech samples from native language speakers. An independentmeasure to determine the ground truth of the native fluency may beacquired, for example, by self-reporting during the test administration.

FIG. 3 is a diagram illustrating components of a system that may train amachine to learn to generate a test corpus and presenting the testcorpus to a user for evaluation. FIG. 3 illustrates a machine learningcomponent for heteronyms model. At 304, native speaker recorded data,for example, including the sound and text, may be acquired. Such datamay be received or retrieved from call center databases, musicdatabases, databases that store people reading text, crowd sourced data,and/or others. Language specific dictionary of heteronyms and targetsentences 302 may be also accessed. At 306, the native speaker recordeddata is parsed and heteronyms are isolated from the native speakerrecorded data. Parsing the speaker recorded data and isolating (oridentifying) heteronyms may be performed by executing speech processingand natural language processing techniques. For example, speechoccurrence of the heteronyms from the dictionary 302 may be recognizedin the native speaker recorded data and isolated. At 308, linguisticfeatures may be extracted from the speech (the native speaker recordeddata acquired at 304), particularly, the heteronyms. Examples oflinguistic features may include phoneme duration, intonation, timing,and loudness. At 310, the extracted features are used to build orgenerate a language dependent model for heteronyms. In one embodiment,each language uses a different machine learning model, chosen by theircharacteristics, using meta learning. These features are used totraining a machine learning model such as Deep Learning, Naïve Bayes andRandom Forest. The output of the machine learning model is a score thatwill inform how a speech (for example, of a person) is in that language,for example, how an input speech utterance compares to a nativespeaker's utterance of the same speech or words. A score, for example,reflects the closeness of an input utterance to the native speaker'sutterance.

At 312, the language dependent model that is generated is executed toscore speech utterance, e.g., received from a user via a user interface,for instance, that is coupled to a microphone or the like that receivesuser's speech. For example, a test corpus of words and/or sentencesincluding the words and/or heteronyms used in building the machinelearning model may be created, and presented to a user via a userinterface. The user may be allowed to read or utter the test corpus ofwords, and the user interface detects or receives the speech utterance.The speech utterance may be input to the machine learning model and themachine learning model may be executed, wherein the machine learningmodel outputs a score representing the language fluency level of theuser.

At 314, the machine learning language dependent model may be applied toa corpus of new words, for example, received or accessed from a databaseof corpus of words (e.g., dictionaries, lexical resources, web pages,blogs, news, forums, text such as portable document format (pdf)documents stored as electronic data on one or more computers and/orcomputer storage devices, and/or others). In this way, the model isretrained or modified to be able to score the corpus of new words.

At 316, a new test set may be created with a new set of heteronyms, forexample, from the corpus of new words 318. For instance, the new wordsare added to the test corpus. In one aspect, the language dependentmachine learning model may retrain itself autonomously or automaticallybased on detecting the new set of heteronyms. For instance, a hardwareprocessor executing the machine learning model may automatically detectthe availability of new words 318 and automatically invoke theretraining.

FIG. 4 illustrates a schematic of an example computer or processingsystem that may implement a system in one embodiment of the presentdisclosure. The computer system is only one example of a suitableprocessing system and is not intended to suggest any limitation as tothe scope of use or functionality of embodiments of the methodologydescribed herein. The processing system shown may be operational withnumerous other general purpose or special purpose computing systemenvironments or configurations. Examples of well-known computingsystems, environments, and/or configurations that may be suitable foruse with the processing system shown in FIG. 4 may include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, handheld or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

The computer system may be described in the general context of computersystem executable instructions, such as program modules, being executedby a computer system. Generally, program modules may include routines,programs, objects, components, logic, data structures, and so on thatperform particular tasks or implement particular abstract data types.The computer system may be practiced in distributed cloud computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network. In a distributed cloudcomputing environment, program modules may be located in both local andremote computer system storage media including memory storage devices.

The components of computer system may include, but are not limited to,one or more processors or processing units 12, a system memory 16, and abus 14 that couples various system components including system memory 16to processor 12. The processor 12 may include a module 30 that performsthe methods described herein. The module 30 may be programmed into theintegrated circuits of the processor 12, or loaded from memory 16,storage device 18, or network 24 or combinations thereof.

Bus 14 may represent one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnects (PCI) bus.

Computer system may include a variety of computer system readable media.Such media may be any available media that is accessible by computersystem, and it may include both volatile and non-volatile media,removable and non-removable media.

System memory 16 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) and/or cachememory or others. Computer system may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 18 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(e.g., a “hard drive”). Although not shown, a magnetic disk drive forreading from and writing to a removable, non-volatile magnetic disk(e.g., a “floppy disk”), and an optical disk drive for reading from orwriting to a removable, non-volatile optical disk such as a CD-ROM,DVD-ROM or other optical media can be provided. In such instances, eachcan be connected to bus 14 by one or more data media interfaces.

Computer system may also communicate with one or more external devices26 such as a keyboard, a pointing device, a display 28, etc.; one ormore devices that enable a user to interact with computer system; and/orany devices (e.g., network card, modem, etc.) that enable computersystem to communicate with one or more other computing devices. Suchcommunication can occur via Input/Output (I/O) interfaces 20.

Still yet, computer system can communicate with one or more networks 24such as a local area network (LAN), a general wide area network (WAN),and/or a public network (e.g., the Internet) via network adapter 22. Asdepicted, network adapter 22 communicates with the other components ofcomputer system via bus 14. It should be understood that although notshown, other hardware and/or software components could be used inconjunction with computer system. Examples include, but are not limitedto: microcode, device drivers, redundant processing units, external diskdrive arrays, RAID systems, tape drives, and data archival storagesystems, etc.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements, if any, in the claims below areintended to include any structure, material, or act for performing thefunction in combination with other claimed elements as specificallyclaimed. The description of the present invention has been presented forpurposes of illustration and description, but is not intended to beexhaustive or limited to the invention in the form disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the invention.The embodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

1.-6. (canceled)
 7. A computer readable storage medium storing a programof instructions executable by a machine to perform a method comprising:training a machine by a machine learning technique for recognizingspeech utterance to determine language fluency level of a user, thetraining comprising: retrieving native speaker recorded data from adatabase; retrieving language specific dictionary of heteronyms; parsingthe native speaker recorded data and isolating the heteronyms from thenative speaker recorded data; extracting linguistic features from thenative speaker recorded data including at least linguistic featuresassociated with the heteronyms; and generating a language dependentmachine learning model based on the linguistic features; generating atest corpus of words comprising at least the heteronyms; receiving atest speech utterance of a user uttering the test corpus of words; andinputting the test speech utterance to the language dependent machinelearning model and executing the language dependent machine learningmodel, wherein the language dependent machine learning model outputs ascore representing the language fluency level of the user.
 8. Thecomputer readable storage medium of claim 7, wherein the linguisticfeatures comprise phoneme duration, intonation, timing, and loudness. 9.The computer readable storage medium of claim 7, wherein the languagedependent machine learning model comprises a deep learning model. 10.The computer readable storage medium of claim 7, wherein the languagedependent machine learning model comprises a naïve Bayes model.
 11. Thecomputer readable storage medium of claim 7, wherein the languagedependent machine learning model comprises a random forest model. 12.The computer readable storage medium of claim 7, further comprisingautomatically retraining the language dependent machine learning modelbased on detecting a new set of heteronyms.
 13. A system of training amachine that recognizes native speech utterance, comprising: a hardwareprocessor; a storage device communicatively coupled to the hardwareprocessor and storing native speaker recorded data; the hardwareprocessor executing a machine learning technique to train the hardwareprocessor to recognize speech utterance to determine language fluencylevel of a user, the training comprising the hardware processor:retrieving native speaker recorded data from the storage device;retrieving language specific dictionary of heteronyms; parsing thenative speaker recorded data and isolating the heteronyms from thenative speaker recorded data; extracting linguistic features from thenative speaker recorded data including at least linguistic featuresassociated with the heteronyms; and generating a language dependentmachine learning model based on the linguistic features; the hardwareprocessor further performing: generating a test corpus of wordscomprising at least the heteronyms; receiving a test speech utterance ofa user uttering the test corpus of words; and inputting the test speechutterance to the language dependent machine learning model and executingthe language dependent machine learning model, wherein the languagedependent machine learning model outputs a score representing thelanguage fluency level of the user.
 14. The system of claim 13, furthercomprising a user interface display coupled to the hardware processor,wherein the test corpus of words is presented to the user via the userinterface display for the user to utter the test corpus of words. 15.The system of claim 13, wherein the linguistic features comprise phonemeduration, intonation, timing, and loudness.
 16. The system of claim 13,wherein the language dependent machine learning model comprises a deeplearning model.
 17. The system of claim 13, wherein the languagedependent machine learning model comprises a naïve Bayes model.
 18. Thesystem of claim 13, wherein the language dependent machine learningmodel comprises a random forest model.
 19. The system of claim 13,wherein the hardware processor automatically retraining the languagedependent machine learning model based on automatically detecting a newset of heteronyms.